{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Hybrid_reverse_channel_coding_of_diagonal_Gaussian_ICML_version.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ww7poC7BCpOW"
      },
      "source": [
        "## Hybrid coding scheme for diagonal Gaussians"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "```\n",
        "Copyright 2022 Google LLC.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License.\n",
        "```"
      ],
      "metadata": {
        "id": "cTvlgl0QFO0o"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pkMGALtqD_Ui"
      },
      "source": [
        "import numpy as np\n",
        "import scipy as sp\n",
        "import scipy.stats\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pFhhnepZErDk"
      },
      "source": [
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h08z9aRmr25T"
      },
      "source": [
        "Below are basic implementations of two general reverse channel coding schemes based on the Poisson functional representation (Li & El Gamal, 2018) and the hybrid coding scheme (Theis & Yosri, 2022). These implementations are meant for educational purposes and were not designed for production use.\n",
        "\n",
        "Using these functions, a sample from a continuous target distribution $q$ can be encoded into a low-entropy integer code. It is assumed that the encoder and decoder have shared knowledge of a proposal distribution $p$ and a random seed (given in the form of `rs` below)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TTF584Al7yhi"
      },
      "source": [
        "def sample_pfr(q, p, w_min, rs=np.random.RandomState(0)):\n",
        "  \"\"\"\n",
        "  Reverse channel coding scheme based on the Poisson functional representation\n",
        "  (Li & El Gamal, 2018).\n",
        "\n",
        "  Parameters\n",
        "  ----------\n",
        "  q: sp.stats.rv_continuous\n",
        "    Target distribution\n",
        "\n",
        "  p: sp.stats.rv_continuous\n",
        "    Prior distribution known to decoder\n",
        "\n",
        "  w_min: float\n",
        "    Lower bound on the density ratio, ideally inf_z p(z) / q(z)\n",
        "\n",
        "  Returns\n",
        "  -------\n",
        "  tuple\n",
        "    Returns a sample, its code, and the number of iterations that were needed\n",
        "    to identify the code.\n",
        "  \"\"\"\n",
        "\n",
        "  if w_min > 1.0 or w_min < 0.0 or np.isnan(w_min):\n",
        "    raise ValueError('`w_min` should not be outside the range [0, 1]')\n",
        "\n",
        "  t = 0  # Poisson process\n",
        "  s = np.inf  # score of current best candidate\n",
        "  n = 0  # index of last accepted proposal\n",
        "  i = 0  # index of current proposal considered\n",
        "  z = None  # value of last accepted sample\n",
        "\n",
        "  while s > t * w_min:\n",
        "    # draw candidate from proposal distribution\n",
        "    z_ = p.rvs(random_state=rs)\n",
        "\n",
        "    # advance Poisson process\n",
        "    t += rs.exponential()\n",
        "\n",
        "    # evaluate candidate\n",
        "    s_ = t * np.prod(p.pdf(z_) / q.pdf(z_))\n",
        "\n",
        "    # accept/reject candidate\n",
        "    if s_ < s:\n",
        "      n = i\n",
        "      s = s_\n",
        "      z = z_\n",
        "\n",
        "    i += 1\n",
        "\n",
        "  return {'sample': z, 'code': n, 'iterations': i}\n",
        "\n",
        "def sample_hybrid(q, p, w_min, M, rs=np.random.RandomState(0)):\n",
        "  \"\"\"\n",
        "  Hybrid reverse channel coding scheme based on universal quantization\n",
        "  and the Poisson functional representation (Theis & Yosri, 2022).\n",
        "\n",
        "  This implementation assumes that q.a and q.b indicate the support of the\n",
        "  distribution. Note that Scipy's truncated distributions implement q.a and\n",
        "  q.b but these do *not* directly correspond to the distributions' support.\n",
        "\n",
        "  Parameters\n",
        "  ----------\n",
        "  q: sp.stats.rv_continuous\n",
        "    Target distribution with finite support indicated by q.a and q.b\n",
        "\n",
        "  p: sp.stats.rv_continuous\n",
        "    Prior distribution known to decoder\n",
        "\n",
        "  w_min: float\n",
        "    Lower bound on the density ratio, ideally inf_z p(z) / q(z)\n",
        "\n",
        "  M: np.ndarray\n",
        "    After transformation, the support of p is at least M times the support of q\n",
        "\n",
        "  Returns\n",
        "  -------\n",
        "  tuple\n",
        "    Returns (z, n, k, i), where (n, k) is a code for the sample z and\n",
        "    i is the number of iterations that were needed to find the code.\n",
        "  \"\"\"\n",
        "\n",
        "  if w_min > 1.0 or w_min < 0.0 or np.isnan(w_min):\n",
        "    raise ValueError('`w_min` should not be outside the range [0, 1]')\n",
        "\n",
        "  dim = p.mean().size\n",
        "\n",
        "  # transformation, its inverse, and its log-derivative\n",
        "  phi = lambda u: p.ppf(u / M)\n",
        "  phi_inv = lambda z: p.cdf(z) * M\n",
        "  log_phi_prime = lambda u: -p.logpdf(phi(u)) - np.log(M)\n",
        "\n",
        "  # transform target distribution\n",
        "  q_phi_logpdf = lambda u: q.logpdf(phi(u)) + log_phi_prime(u)\n",
        "\n",
        "  # transform support of target distribution\n",
        "  q_phi_support = phi_inv(np.asarray([q.a, q.b]))\n",
        "\n",
        "  if np.any(np.abs(np.diff(q_phi_support, axis=0)) > 1):\n",
        "    raise ValueError('The support of the target distribution is too large')\n",
        "\n",
        "  # center of bridge proposal distribution\n",
        "  c = np.mean(q_phi_support, axis=0)\n",
        "\n",
        "  # don't generate proposals outside support of p\n",
        "  c = np.clip(c, 0.5, M - 0.5)\n",
        "  \n",
        "  # apply reverse channel coding\n",
        "  t = 0  # Poisson process\n",
        "  s = np.inf  # score of last accepted proposal\n",
        "  n = 0  # index of last accepted proposal\n",
        "  i = 0  # index of current proposal\n",
        "\n",
        "  while np.exp(s) > t * w_min * np.prod(M):\n",
        "    # generate candidate using universal quantization\n",
        "    u = rs.rand(dim)\n",
        "    k_ = np.array(c - u + 0.5, dtype=int)  # equals round(c - u) since c - u + 0.5 >= 0\n",
        "    y_ = k_ + u\n",
        "\n",
        "    # evaluate candidate\n",
        "    t += rs.exponential()\n",
        "    s_ = np.log(t) - np.sum(q_phi_logpdf(y_))\n",
        "\n",
        "    # accept/reject candidate\n",
        "    if s_ < s:\n",
        "      n = i\n",
        "      s = s_\n",
        "      k = k_\n",
        "      y = y_\n",
        "\n",
        "    i += 1\n",
        "\n",
        "  # transform sample back\n",
        "  z = phi(y)\n",
        "\n",
        "  # n and k contain all the information needed to produce z\n",
        "  return {\n",
        "    'sample': z,\n",
        "    'code': (n, np.asarray(k, dtype=int)),\n",
        "    'iterations': i}"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7I11IiLpNyY"
      },
      "source": [
        "def decode_hybrid(n, k, p, M, rs=np.random.RandomState(0)):\n",
        "  \"\"\"\n",
        "  Accepts a code representing a sample and decodes it.\n",
        "\n",
        "  Parameters\n",
        "  ----------\n",
        "  n: int\n",
        "    The index of the candidate\n",
        "\n",
        "  k: int\n",
        "    Additional information required to generate the candidate\n",
        "\n",
        "  p: sp.stats.rv_continuous\n",
        "    Assumed marginal distribution of the samples\n",
        "\n",
        "  M: np.ndarray\n",
        "    See Theis & Yosri (2022) for an explanation of this parameter\n",
        "\n",
        "  Returns\n",
        "  -------\n",
        "  numpy.ndarray\n",
        "    A decoded sample\n",
        "  \"\"\"\n",
        "\n",
        "  dim = p.mean().size\n",
        "\n",
        "  # transformation, its inverse, and its log-derivative\n",
        "  phi = lambda u: p.ppf(u / M)\n",
        "\n",
        "  # advance random seed\n",
        "  for _ in range(n + 1):\n",
        "    u = rs.rand(dim)\n",
        "    rs.exponential()\n",
        "\n",
        "  return phi(k + u)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NtuAnuUAsAi6"
      },
      "source": [
        "The code below deals with the special case of diagonal Gaussian target and proposal distributions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IbNW0TsoEw-F"
      },
      "source": [
        "def minimum_weight(q, p):\n",
        "  \"\"\"\n",
        "  Returns min_z p(z) / q(z) for normal distributions q and p.\n",
        "  \"\"\"\n",
        "  assert np.all(p.var() >= q.var())\n",
        "\n",
        "  with np.errstate(invalid='ignore'):\n",
        "    x_min = (p.var() * q.mean() - q.var() * p.mean()) / (p.var() - q.var())\n",
        "    w_min = p.logpdf(x_min) - q.logpdf(x_min)\n",
        "\n",
        "  return np.where(np.isnan(w_min), 1.0, np.exp(w_min))\n",
        "\n",
        "def sample_gaussian(q, p, D=1e-4, rs=np.random.RandomState(0)):\n",
        "  \"\"\"\n",
        "  Encodes diagonal Gaussian using hybrid coding scheme.\n",
        "\n",
        "  Parameters\n",
        "  ----------\n",
        "  q: sp.stats.rv_continuous\n",
        "    Target normal distribution\n",
        "\n",
        "  p: sp.stats.rv_continuous\n",
        "    Marginal normal distribution\n",
        "\n",
        "  D: float\n",
        "   Controls fraction of mass of q truncated\n",
        " \n",
        "  rs: numpy.random.RandomState\n",
        "    Source of randomness shared between encoder and decoder\n",
        "\n",
        "  Returns\n",
        "  -------\n",
        "  dict\n",
        "    A dictionary containing the a sample, its code and additional information\n",
        "  \"\"\"\n",
        "\n",
        "  dim = q.mean().size\n",
        "\n",
        "  # adjust for dimensionality\n",
        "  D = 1.0 - np.power(1.0 - D, 1.0 / dim)\n",
        "\n",
        "  # support of standard truncated normal\n",
        "  a = sp.stats.norm().ppf(D / 2.0)\n",
        "  b = sp.stats.norm().ppf(1.0 - D / 2.0)\n",
        "\n",
        "  # approximate normal with truncated normal\n",
        "  q_tr = sp.stats.truncnorm(a=a, b=b, loc=q.mean(), scale=q.std())\n",
        "\n",
        "  # fix broken support indicators\n",
        "  q_tr.a = q_tr.a * q_tr.std() + q_tr.mean()\n",
        "  q_tr.b = q_tr.b * q_tr.std() + q_tr.mean()\n",
        "\n",
        "  # determine support of widest q (after transformation with p.cdf)\n",
        "  c = p.cdf(p.mean() - q_tr.mean() + q_tr.a)\n",
        "  d = p.cdf(p.mean() - q_tr.mean() + q_tr.b)\n",
        "  M = np.asarray(np.floor(1 / (d - c)), dtype=int)\n",
        "\n",
        "  # lower bound on w_min = min_z p(z) / q_tr(z)\n",
        "  w_min = np.prod(minimum_weight(q, p) * (1 - D)) \n",
        "  results = sample_hybrid(q_tr, p, w_min, M, rs)\n",
        "  results['factor'] = M\n",
        "  return results"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZ5yOBoTwMqT"
      },
      "source": [
        "The following cell encodes Gaussian samples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XX8PCtLH72_9"
      },
      "source": [
        "mean_scale = np.asarray([40.0, 20.0])\n",
        "target_scale = np.asarray([1.0, 1.0])\n",
        "\n",
        "# proposal distribution\n",
        "p = sp.stats.norm(\n",
        "    loc=[0, 0],\n",
        "    scale=np.sqrt(target_scale ** 2 + mean_scale ** 2))\n",
        "\n",
        "# target distributions\n",
        "targets = []\n",
        "for _ in range(2000):\n",
        "  target_mean = np.random.randn(2) * mean_scale\n",
        "  targets.append(sp.stats.norm(loc=target_mean, scale=target_scale))\n",
        "\n",
        "# encode samples\n",
        "samples = [\n",
        "  sample_gaussian(q, p, D=1e-4, rs=np.random.RandomState(i))\n",
        "  for i, q in enumerate(targets)]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following cell decodes the Gaussian samples. While it is not obvious from the implementation of `sample_gaussian`, the factor `M` is independent of the mean of `q`. This allows us to use it in the decoding step below."
      ],
      "metadata": {
        "id": "ZrGwRb7q39iA"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQP9d_kacorl"
      },
      "source": [
        "# decode samples\n",
        "z = [\n",
        "  decode_hybrid(\n",
        "      n=s['code'][0],\n",
        "      k=s['code'][1],\n",
        "      p=p,\n",
        "      M=s['factor'],\n",
        "      rs=np.random.RandomState(i))\n",
        "  for i, s in enumerate(samples)]\n",
        "z = np.asarray(z)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nj4TB2CcdnYu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "9cd0f547-c9c1-4d96-9a51-760ee9394f4a"
      },
      "source": [
        "t = np.linspace(0, 2 * np.pi, 100)\n",
        "x = np.cos(t) * p.std()[0] * 2\n",
        "y = np.sin(t) * p.std()[1] * 2\n",
        "\n",
        "# visualize prior distribution and samples\n",
        "plt.figure(figsize=(5, 5))\n",
        "plt.plot(x, y, 'k')\n",
        "xl, yl = plt.xlim(), plt.ylim()\n",
        "plt.plot(z[:, 0], z[:, 1], '.')\n",
        "plt.axis('equal')\n",
        "plt.xlim(*xl)\n",
        "plt.ylim(*yl);"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 321,
              "height": 302
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7toFg5h_wTPj"
      },
      "source": [
        "Below we estimate the cost of encoding the samples, and check that the cost agrees with the theoretical bound on the coding cost."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XkjCXp9Eb4D1"
      },
      "source": [
        "def coding_cost_hybrid(q, p, sample):\n",
        "  \"\"\"\n",
        "  Estimate the coding cost (assuming a Zipf distribution)\n",
        "  as well as theoretical upper and lower bounds.\n",
        "\n",
        "  The target and marginal distribution are used to compute the bounds\n",
        "  and the parameter of the Zipf distribution used to encode the sample.\n",
        "  It is assumed that the entropy of `q` is the same across\n",
        "  target distributions and that `p` is the marginal distribution, i.e.,\n",
        "  E[q_X(z)] = p(z).\n",
        "\n",
        "  Parameters\n",
        "  ----------\n",
        "  q: sp.stats.rv_continuous\n",
        "    Target distribution\n",
        "\n",
        "  p: sp.stats.rv_continuous\n",
        "    Marginal distribution\n",
        "\n",
        "  sample: dict\n",
        "    A single sample as produced by `sample_hybrid`\n",
        "\n",
        "  Returns\n",
        "  -------\n",
        "  dict\n",
        "    Coding cost and bounds\n",
        "  \"\"\"\n",
        "\n",
        "  # mutual information between source and communicated sample\n",
        "  marg_diff_entropy = p.entropy().sum() / np.log(2)\n",
        "  cond_diff_entropy = q.entropy().sum() / np.log(2)\n",
        "  mi = marg_diff_entropy - cond_diff_entropy\n",
        "\n",
        "  # upper bound on entropy\n",
        "  log2M = np.log2(sample['factor']).sum()\n",
        "  coding_cost_bound = mi + np.log2(mi - log2M + 1) + 4\n",
        "\n",
        "  # coding cost of N under Zipf distribution\n",
        "  exponent = 1.0 + 1.0 / (1.0 + np.log2(np.e) / np.e + mi - log2M)\n",
        "  log2Pn = -exponent * np.log2(sample['code'][0] + 1)\n",
        "  log2Pn = log2Pn - np.log2(sp.special.zeta(exponent))\n",
        "\n",
        "  # coding cost of N and K (log2M is the cost of encoding K)\n",
        "  coding_cost_zipf = -log2Pn + log2M\n",
        "\n",
        "  return {\n",
        "      'zipf': coding_cost_zipf,\n",
        "      'lower_bound': mi,\n",
        "      'upper_bound': coding_cost_bound,\n",
        "  }"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LyK9_abRibV9",
        "outputId": "5436874c-7c51-430e-890e-67c7dc546cf4"
      },
      "source": [
        "coding_costs = [coding_cost_hybrid(q, p, s) for q, s in zip(targets, samples)]\n",
        "print('{:.4f} <= {:.4f} <= {:.4f}'.format(\n",
        "    coding_costs[0]['lower_bound'],\n",
        "    np.mean([c['zipf'] for c in coding_costs]),\n",
        "    coding_costs[0]['upper_bound']))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9.6461 <= 12.2743 <= 15.8084\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hhV4QOB8V3O"
      },
      "source": [
        "Below we measure the computational efficiency of the hybrid coding scheme."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJUWJT2Q8VSU",
        "outputId": "2139a9c3-040d-4e0e-d4a9-bc3d99950ad4"
      },
      "source": [
        "np.random.seed(1)\n",
        "\n",
        "dim = 2\n",
        "mean_scales = np.linspace(0, 50, 8)\n",
        "num_samples = 200\n",
        "coding_costs = []\n",
        "\n",
        "samples = []\n",
        "targets = []\n",
        "\n",
        "for sigma in tqdm(mean_scales):\n",
        "  mean_scale = np.ones(dim) * sigma\n",
        "  target_scale = np.ones(dim)\n",
        "\n",
        "  # prior/marginal distribution of communicated sample\n",
        "  p = sp.stats.norm(\n",
        "    loc=np.zeros(dim),\n",
        "    scale=np.sqrt(target_scale ** 2 + mean_scale ** 2))\n",
        "\n",
        "  # draw random target distributions\n",
        "  targets.append([])\n",
        "  for _ in range(num_samples):\n",
        "    target_mean = np.random.randn(dim) * mean_scale\n",
        "    targets[-1].append(sp.stats.norm(loc=target_mean, scale=target_scale))\n",
        "\n",
        "  # encode samples\n",
        "  samples.append([\n",
        "      sample_gaussian(\n",
        "          q,\n",
        "          p,\n",
        "          D=1e-4,\n",
        "          rs=np.random.RandomState(i))\n",
        "      for i, q in enumerate(targets[-1])])\n",
        "  coding_costs.append([\n",
        "      coding_cost_hybrid(q, p, s)\n",
        "      for q, s in zip(targets[-1], samples[-1])])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 8/8 [01:33<00:00, 11.72s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbbdNJQR_TyB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "ad82275f-589f-4509-b55e-f9706170d3ce"
      },
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "\n",
        "# plot coding costs\n",
        "plt.subplot(1, 2, 1)\n",
        "\n",
        "coding_cost_lower = [cc[0]['lower_bound'] for cc in coding_costs]  # mutual information\n",
        "coding_cost_upper = [cc[0]['upper_bound'] for cc in coding_costs]\n",
        "coding_cost_zipf = [np.mean([c['zipf'] for c in cc]) for cc in coding_costs]\n",
        "\n",
        "plt.plot(mean_scales, coding_cost_zipf, 'r')\n",
        "plt.plot(mean_scales, coding_cost_lower, color=(0.5, 0, 0), ls='--')\n",
        "plt.plot(mean_scales, coding_cost_upper, color=(0.5, 0, 0), ls='--')\n",
        "plt.xlabel(r'$\\sigma$', fontsize=14)\n",
        "plt.ylabel('Coding cost [bit]', fontsize=14)\n",
        "\n",
        "# plot computational cost\n",
        "plt.subplot(1, 2, 2)\n",
        "\n",
        "num_iter_hybrid_10 = [np.percentile([s['iterations'] for s in ss], 10) for ss in samples]\n",
        "num_iter_hybrid_25 = [np.percentile([s['iterations'] for s in ss], 25) for ss in samples]\n",
        "num_iter_hybrid_50 = [np.median([s['iterations'] for s in ss]) for ss in samples]\n",
        "num_iter_hybrid_75 = [np.percentile([s['iterations'] for s in ss], 75) for ss in samples]\n",
        "num_iter_hybrid_90 = [np.percentile([s['iterations'] for s in ss], 90) for ss in samples]\n",
        "num_iter_mean = [np.mean([s['iterations'] for s in ss]) for ss in samples]\n",
        "\n",
        "plt.plot(mean_scales, num_iter_hybrid_50, 'r')\n",
        "plt.plot(mean_scales, num_iter_mean, 'r--')\n",
        "plt.fill_between(\n",
        "    np.hstack([mean_scales, mean_scales[::-1]]),\n",
        "    np.hstack([num_iter_hybrid_25, num_iter_hybrid_75[::-1]]),\n",
        "    color='r',\n",
        "    alpha=0.1,\n",
        "    lw=0)\n",
        "plt.xlabel(r'$\\sigma$', fontsize=14)\n",
        "plt.ylabel('Computational cost [#proposals]', fontsize=13)\n",
        "plt.tight_layout();"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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}